code: def __init__(self, dataset, num_layers, hidden):
      super().__init__()
      self.conv1 = GINConv(
          Sequential(
              Linear(dataset.num_features, hidden),
              ReLU(),
              BN(hidden),
              Linear(hidden, hidden),
              ReLU(),
              BN(hidden),
          ),
          train_eps=True,
      )
      self.convs = torch.nn.ModuleList()
      for i in range(num_layers - 1):
          self.convs.append(
              GINConv(
                  Sequential(
                      Linear(hidden, hidden),
                      ReLU(),
                      BN(hidden),
                      Linear(hidden, hidden),
                      ReLU(),
                      BN(hidden),
                  ),
                  train_eps=True,
              )
          )
      self.lin1 = Linear(hidden, hidden)
      self.lin2 = Linear(hidden, dataset.num_classes)
  
imports: import torch
  import torch.nn.functional
  from torch.nn import BatchNorm1d
  from torch.nn import Linear, ReLU, Sequential
  from torch_geometric.nn import GINConv, JumpingKnowledge, global_mean_pool
label_desc: Use image node_id 1 to create a new GIN object from inherited base classes: torch.nn with example: obj = GIN(dataset, num_layers, hidden)